Table 4 Technical efficiency of PHC system based on Bootstrap-DEA, 2016–2020
Region | 2016 | 2017 | 2018 | 2019 | 2020 | Mean | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Traditional | Correction | Traditional | Correction | Traditional | Correction | Traditional | Correction | Traditional | Correction | Â | |
Beijing | 1.000 | 0.826 | 1.000 | 0.840 | 1.000 | 0.825 | 1.000 | 0.766 | 1.000 | 0.750 | 0.801 |
Tianjin | 1.000 | 0.958 | 1.000 | 0.933 | 1.000 | 0.895 | 1.000 | 0.840 | 1.000 | 0.909 | 0.907 |
Hebei | 1.000 | 0.920 | 1.000 | 0.938 | 0.992 | 0.962 | 0.941 | 0.886 | 0.856 | 0.808 | 0.903 |
Shanxi | 0.545 | 0.531 | 0.534 | 0.515 | 0.583 | 0.561 | 0.499 | 0.464 | 0.493 | 0.460 | 0.506 |
Inner Mongolia | 0.594 | 0.577 | 0.594 | 0.577 | 0.610 | 0.586 | 0.500 | 0.465 | 0.499 | 0.474 | 0.536 |
Liaoning | 0.641 | 0.627 | 0.655 | 0.638 | 0.668 | 0.653 | 0.637 | 0.609 | 0.560 | 0.543 | 0.614 |
Jilin | 0.489 | 0.475 | 0.526 | 0.514 | 0.515 | 0.501 | 0.491 | 0.465 | 0.424 | 0.400 | 0.471 |
Heilongjiang | 0.735 | 0.717 | 0.714 | 0.698 | 0.557 | 0.540 | 0.482 | 0.456 | 0.416 | 0.397 | 0.562 |
Shanghai | 1.000 | 0.837 | 1.000 | 0.836 | 1.000 | 0.826 | 1.000 | 0.765 | 1.000 | 0.755 | 0.804 |
Jiangsu | 1.000 | 0.879 | 1.000 | 0.834 | 1.000 | 0.848 | 1.000 | 0.815 | 1.000 | 0.829 | 0.841 |
Zhejiang | 1.000 | 0.851 | 1.000 | 0.828 | 1.000 | 0.839 | 1.000 | 0.773 | 1.000 | 0.739 | 0.806 |
Anhui | 1.000 | 0.945 | 1.000 | 0.901 | 1.000 | 0.889 | 1.000 | 0.776 | 1.000 | 0.759 | 0.854 |
Fujian | 0.770 | 0.750 | 0.741 | 0.719 | 0.766 | 0.744 | 0.785 | 0.742 | 0.861 | 0.818 | 0.755 |
Jiangxi | 1.000 | 0.907 | 1.000 | 0.872 | 1.000 | 0.878 | 1.000 | 0.853 | 1.000 | 0.896 | 0.881 |
Shandong | 1.000 | 0.841 | 1.000 | 0.842 | 1.000 | 0.834 | 1.000 | 0.872 | 1.000 | 0.749 | 0.828 |
Henan | 1.000 | 0.933 | 1.000 | 0.917 | 1.000 | 0.939 | 0.966 | 0.888 | 1.000 | 0.756 | 0.887 |
Hubei | 1.000 | 0.870 | 1.000 | 0.908 | 1.000 | 0.914 | 1.000 | 0.893 | 1.000 | 0.941 | 0.905 |
Hunan | 1.000 | 0.887 | 1.000 | 0.907 | 1.000 | 0.876 | 1.000 | 0.818 | 1.000 | 0.824 | 0.862 |
Guangdong | 1.000 | 0.836 | 1.000 | 0.829 | 1.000 | 0.834 | 1.000 | 0.777 | 1.000 | 0.743 | 0.804 |
Guangxi | 1.000 | 0.942 | 0.987 | 0.959 | 0.993 | 0.968 | 1.000 | 0.902 | 1.000 | 0.912 | 0.937 |
Hainan | 0.936 | 0.899 | 0.906 | 0.869 | 0.904 | 0.864 | 0.901 | 0.834 | 0.929 | 0.879 | 0.869 |
Chongqing | 1.000 | 0.904 | 1.000 | 0.890 | 1.000 | 0.897 | 1.000 | 0.869 | 1.000 | 0.878 | 0.888 |
Sichuan | 1.000 | 0.825 | 1.000 | 0.827 | 1.000 | 0.842 | 1.000 | 0.782 | 1.000 | 0.748 | 0.805 |
Guizhou | 0.761 | 0.742 | 0.744 | 0.725 | 0.813 | 0.794 | 0.835 | 0.791 | 0.729 | 0.699 | 0.750 |
Yunnan | 1.000 | 0.939 | 0.954 | 0.921 | 0.969 | 0.937 | 1.000 | 0.924 | 1.000 | 0.938 | 0.932 |
Tibet | 1.000 | 0.895 | 1.000 | 0.905 | 1.000 | 0.853 | 1.000 | 0.784 | 1.000 | 0.810 | 0.849 |
Shaanxi | 0.727 | 0.704 | 0.774 | 0.750 | 0.771 | 0.745 | 0.748 | 0.700 | 0.654 | 0.627 | 0.705 |
Gansu | 1.000 | 0.940 | 1.000 | 0.922 | 1.000 | 0.919 | 0.987 | 0.914 | 1.000 | 0.933 | 0.926 |
Qinghai | 1.000 | 0.833 | 1.000 | 0.847 | 1.000 | 0.842 | 1.000 | 0.848 | 1.000 | 0.903 | 0.855 |
Ningxia | 1.000 | 0.848 | 1.000 | 0.851 | 1.000 | 0.861 | 1.000 | 0.823 | 1.000 | 0.789 | 0.834 |
Xinjiang | 0.930 | 0.905 | 0.852 | 0.822 | 0.770 | 0.744 | 0.799 | 0.750 | 0.729 | 0.702 | 0.785 |
Mean | 0.891 | 0.813 | 0.887 | 0.807 | 0.884 | 0.802 | 0.867 | 0.755 | 0.847 | 0.735 | Â |